| 模糊近似支持向量机模型及其在信用风险评估中的应用 |
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| 引用本文： | 姚潇,余乐安.模糊近似支持向量机模型及其在信用风险评估中的应用[J].系统工程理论与实践,2012,32(3):549-554. |
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| 作者姓名： | 姚潇 余乐安 |
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| 作者单位： | 1. 中国科学院 数学与系统科学研究院, 北京 100190;2. 北京化工大学 经济管理学院, 北京 100029;3. 杭州师范大学 阿里巴巴商学院 电子商务与信息安全重点实验室, 杭州 310036 |
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| 基金项目： | 国家杰出青年科学基金(71025005);国家自然科学基金重大研究计划培育项目(90924024) |
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| 摘 要： | 支持向量机是近些年兴起的人工智能方法,并在信用风险分析领域得到了广泛应用.为了有效地减小在实证研究中样本的奇异点和噪声对模型的干扰, 在近似支持向量机的基础上, 引入模糊隶属度的思想,提出了模糊近似支持向量机, 使之不仅能保留近似支持向量机原有的优点,同时也可以减小奇异点和噪声对模型的干扰,从而进一步提高了分类判别能力. 为验证模糊近似支持向量机的效果, 利用两个公开的信用数据集进行实证研究. 实证研究结果表明:与其它模型相比,所提出的模糊近似支持向量机能够显著地提高信用风险分类精度,具有较高的实用价值.
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| 关 键 词： | 信用风险评估 近似支持向量机 模糊隶属度 |
| 收稿时间： | 2011-04-08 |
A fuzzy proximal support vector machine model and its application to credit risk analysis |
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| YAO Xiao
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YU Le-an.A fuzzy proximal support vector machine model and its application to credit risk analysis[J].Systems Engineering —Theory & Practice,2012,32(3):549-554. |
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| Authors: | YAO Xiao YU Le-an |
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| Institution: | 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;2. School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China;3. Hangzhou Key Laboratory of E-Business and Information Security, Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, China |
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| Abstract: | Support vector machines(SVM),one of the emerging artificial intelligence techniques,has been widely used in credit risk evaluation.In order to reduce the effects of outliers and noise in the datasets effectively,a fuzzy proximal SVM(FPSVM) is proposed based on the proximal SVM(PSVM) and fuzzy memberships theory.Due to the fact that the proposed FPSVM can effectively decrease the disturbance of sample outliers and noise without the loss of the advantages of PSVM,the proposed FPSVM can increase the model performance significantly.For verification and illustration,two public available credit datasets are used to test and compare the performance of different models.The experimental results show the proposed FPSVM model can yield better performances compared with other models listed in this study. |
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| Keywords: | credit risk analysis proximal support vector machine fuzzy memberships |
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